Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform

• Autorzy: Kumar M. , Pachori R. B. , Rajendra Acharya U.

Identyfikatory

DOI

10.1016/j.bbe.2018.04.004

• Języki publikacji: EN

Abstrakty

EN

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT.We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.

Słowa kluczowe

EN

atrial fibrillation; ECG segment; flexible analytic wavelet transform; entropy features; classification

PL

migotanie przedsionków; elektrokardiogram; wybór cech

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 3
• Strony: 564--573
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Kumar M.

• Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India

autor

Pachori R. B.

• Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

autor

Rajendra Acharya U.

• Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

Bibliografia

• [1] Acharya UR, et al. Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads. Knowl-Based Syst 2016;99:146–56.
• [2] Acharya UR, Fujita H, Adam M, Lih OS, Sudarshan VK, Hong TJ, et al. Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: a comparative study. Inf Sci 2017;377:17–29.
• [3] Acharya UR, Fujita H, Lih OS, Hagiwara Y, Tan JH, Adam M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf Sci 2017;405:81–90.
• [4] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al. A deep convolutional neural network model to classify heartbeats. Comput Biol Med 2017;89:389–96.
• [5] Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med 2015;60:132–42.
• [6] Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 2014;24:1163–77.
• [7] Babaeizadeh S, Gregg RE, Helfenbein ED, Lindauer JM, Zhou SH. Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol 2009;42:522–6.
• [8] Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 2002;88:174102.
• [9] Bayram I. An analytic wavelet transform with a flexible time-frequency covering. IEEE Trans Signal Process 2013;61:1131–42.
• [10] Bayram I. An analytic wavelet transform with a flexible time-frequency covering. Available at: http://web.itu.edu.tr/ibayram/AnDWT/.
• [11] Bayram I, Selesnick IW. Frequency-domain design of overcomplete rational delation wavelet transform. IEEE Trans Signal Process 2009;57:2957–72.
• [12] Box JF. Guinness, Gosset, Fisher, and small samples. Stat Sci 1987;2:45–52.
• [13] Breiman L. Random forests. Mach Learn 2001;45:5–32.
• [14] Cui X, Chang E, Yang WH, Jiang BC, Yang AC, Peng CK. Automated detection of paroxysmal atrial fibrillation using an information-based similarity approach. Entropy 2017;19:677.
• [15] Dash S, Chon KH, Lu S, Raeder EA. Automatic real time detection of atrial fibrillation. Ann Biomed Eng 2009;37:1701–9.
• [16] Du X, Rao N, Qian M, Liu D, Li J, Feng W, et al. A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol 2014;19:217–25.
• [17] Faust O, Hagiwara Y, Tan JH, Oh SL, Acharya UR. Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 2018;161:1–13.
• [18] Fuster V, et al. 2011 ACCF/AHA/HRS focused updates incorporated into the ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation; 123. 2011;p. e269–367.
• [19] Gillis AM, Krahn AD, Skanes AC, Nattel S. Management of atrial fibrillation in the year 2033: new concepts, tools, and applications leading to personalized medicine. Can J Cardiol 2013;29:1141–6.
• [20] Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation 2000;101:e215–20.
• [21] Gupta V, Priya T, Yadav AK, Pachori RB, Acharya UR. Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recogn Lett 2017;94:180–8.
• [22] Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The weka data mining software: an update. ACM SIGKDD Explor Newsl 2009;11:10–8.
• [23] Han J, Dong F, Xu YY. Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement. J Phys: Conf Ser 2009;147:012041.
• [24] Huang C, Ye S, Chen H, Li D, He F, Tu Y. A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans Biomed Eng 2011;58:1113–9.
• [25] Jiang K, Huang C, Ye S, Chen H. High accuracy in automatic detection of atrial fibrillation for Holter monitoring. J Zhejiang Univ-Sci B 2012;13:751–6.
• [26] Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence; 1995. p. 1137–43.
• [27] Kumar M, Pachori RB, Acharya UR. An efficient automated technique for CAD diagnosis using flexible analytic wavelet transform and entropy features extracted from HRV signals. Expert Syst Appl 2016;63:165–72.
• [28] Kumar M, Pachori RB, Acharya UR. Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework. Entropy 2017;19:488.
• [29] Kumar M, Pachori RB, Acharya UR. Characterization of coronary artery disease using flexible analytic wavelet transform applied on ECG signals. Biomed Signal Process Control 2017;31:301–8.
• [30] Kumar M, Pachori RB, Acharya UR. Use of accumulated entropies for automated detection of congestive heart failure in flexible analytic wavelet transform framework based on short-term HRV signals. Entropy 2017;19:92.
• [31] Ladavich S, Ghoraani B. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed Signal Process Control 2015;18:274–81.
• [32] Lake DE, Moorman JR. Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. Am J Physiol Heart Circ Physiol 2011;300:H319–25.
• [33] Lee J, Reyes BA, McManus DD, Mathias O, Chon KH. Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng 2013;60:203–6.
• [34] Mant J, Fitzmaurice DA, Hobbs FDR, Jowett S, Murray ET, Holder R, et al. Accuracy of diagnosing atrial fibrillation on electrocardiogram by primary care practitioners and interpretative diagnostic software: analysis of data from screening for atrial fibrillation in the elderly (SAFE) trial. BMJ 2007;335:380.
• [35] Markides V, Schilling RJ. Atrial fibrillation: Classification, pathophysiology, mechanisms and drug treatment. Heart 2003;89:939–43.
• [36] Martis RJ, Acharya UR, Mandana KM, Ray AK, Chakraborty C. Cardiac decision making using higher order spectra. Biomed Signal Process Control 2013;8:193–203.
• [37] Martis RJ, Acharya UR, Min LC. ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed Signal Process Control 2013;8:437–48.
• [38] McKight PE, Najab J. Kruskal–Wallis Test, Corsini Encyclopedia of Psychology. John Wiley and Sons; 2010.
• [39] Moody GB, Mark RG. A new method for detecting atrial fibrillation using R-R intervals. Comput Cardiol 1983;10:227–30.
• [40] Pachori RB. Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 2008;2008:1–5.
• [41] Pachori RB, Kumar M, Avinash P, Shashank K, Acharya UR. An improved online paradigm for screening of diabetic patients using RR-interval signals. J Mech Med Biol 2016;16:1640003.
• [42] Quinlan JR. Induction of decision trees. Mach Learn 1986;1:81–106.
• [43] Quinlan JR. C4.5: Programs for Machine Learning. San Francisco, CA, USA: Morgan Kaufmann; 1993.
• [44] Rodenas J, Garcia M, Alcaraz R, Rieta JJ. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy 2015;17:6179–99.
• [45] Sharma M, Pachori RB, Acharya UR. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 2017;94:172–9.
• [46] Sharma R, Kumar M, Pachori RB, Acharya UR. Decision support system for focal EEG signals using tunable-Q wavelet transform. J Comput Sci 2017;20:52–60.
• [47] Sharma R, Pachori RB, Acharya UR. An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures. Entropy 2015;17:5218–40.
• [48] Sharma RR, Pachori RB. Time-frequency representation using IEVDHM-HT with application to classification of epileptic EEG signals. IET Sci Meas Technol 2018;12:72–82.
• [49] Slocum J, Sahakian A, Swiryn S. Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. J Electrocardiol 1992;25:1–8.
• [50] Sood S, Kumar M, Pachori RB, Acharya UR. Application of empirical mode decomposition-based features for analysis of normal and CAD heart rate signals. J Mech Med Biol 2016;16:1640002.
• [51] Tateno K, Glass L. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and 4 RR intervals. Med Biol Eng Comput 2001;39:664–71.
• [52] Zhang C, Li B, Chen B, Cao H, Zi Y, He Z. Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mech Syst Signal Process 2015;64-65:162–87.
• [53] Zhou X, Ding H, Ung B, Pickwell-MacPherson E, Zhang Y. Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy. BioMed Eng OnLine 2014;13:18.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).